文章基本信息

标题：IMPROVED FAULT DETECTION IN WATER DESALINATION SYSTEMS USING MACHINE LEARNING TECHNIQUES
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作者：MORCHED DERBALI ; ANAS FATTOUH ; HOUSSEM JERBI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：92
期号：2
出版社：Journal of Theoretical and Applied
摘要：In this paper, the authors have attempted to study the fault detection using the machine learning technique for the water Membrane Distillation Systems (MDS). Initially, an actual system with the MDS, applying nanotechnology was developed which was based on actual measurements. Then, the errors occurring between the outputs of the model (additionally, these outputs serve as MDS inputs) and system outputs were classified for identifying the system faults. This type of classification was carried out by using different approaches and the classification results were further compared. It was noted that the classification accuracy obtained by using the decision trees was the best as compared to the other learning techniques like K-Nearest Neighbours, Neural Networks, and the Support Vector Machines (SVM).
关键词：Learning Techniques; Water Membrane Distillation System; Desalination Systems; Fault Detection; Detection Accuracy.